An ever increasing amount of data and data sources are now available to researchers, analysts, organizational entities, and others. This influx of information allows for sophisticated analysis but, at the same time, presents many new challenges for sifting through the available data and data sources to locate the most relevant and useful information. As the use of technology continues to increase, so, too, will the availability of new data sources and information.
Various methods can be used for analyzing data. Decision trees, one such method, provide a mechanism for evaluating the future result or outcome of multiple different choices or courses of action. To be effective, however, decision trees must be populated with data appropriate to the circumstances and goals of a particular domain. Furthermore, the data used must provide enough accuracy to ensure that predicted eventualities sufficiently model realized outcomes. The tolerance for accuracy of the predictions is highly dependent on the domain and goals of a particular application.
Because of the abundant availability of data from a vast number of data sources, determining the optimal values and sources for use in analytic methods, such as decision trees, presents a complicated problem that is difficult to overcome. The analysis obtained through a decision tree is only as effective as the data used to populate the various metrics under analysis. Accurately utilizing the available data can require both a team of individuals possessing extensive domain expertise as well as many months of work to create useful decision tree models detailing possible outcomes. The process can involve exhaustively searching existing literature, publications, and other available data to identify and study relevant data sources that are available both privately and publicly.
While this approach can often provide effective academic analysis, applying these types of analytical techniques to domains requiring accurate results obtainable only through time and resource intensive research is incompatible with the demands of modern applications. For example, the developed model may not line up with specific circumstances or individual considerations. In this scenario, applying the model requires extrapolation to fit the specific circumstances, diluting the effectiveness of the model, or requires spending valuable time and resources to modify the model. As a result, models developed in this way typically provide only generalized guidance insufficient for use in individualized settings. As more detailed and individualized data becomes available, demand for the ability to accurately discern relevant data points from the sea of available information and efficiently apply that data across thousands of individualized scenarios increases.